system

Abstract

The system according to this embodiment aims to enable physicians to quickly and efficiently acquire and understand necessary medical information. [Solution] The system according to this embodiment comprises a reception unit, a search unit, a summarization unit, and a provision unit. The reception unit receives input from a physician. The search unit searches a medical database based on the information received by the reception unit. The summarization unit summarizes the search results obtained by the search unit. The provision unit provides the information summarized by the summarization unit to the physician.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for doctors to quickly and efficiently search for and understand necessary medical information.

[0005] The system according to the embodiment aims to enable doctors to quickly and efficiently obtain and understand necessary medical information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a search unit, a summarization unit, and a provision unit. The reception unit receives an input from a doctor. The search unit searches a medical database based on the information received by the reception unit. The summarization unit summarizes the search results obtained by the search unit. The provision unit provides the information summarized by the summarization unit to the doctor. [Effects of the Invention]

[0007] The system according to this embodiment allows physicians to quickly and efficiently acquire and understand necessary medical information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The medical information retrieval and summarization system according to an embodiment of the present invention is a system that uses generative AI to provide physicians with efficient and rapid search and summarization of medical papers. This system accepts input from physicians, searches a medical database, and provides a summary of the search results, enabling physicians to efficiently acquire medical information. This system consists of the following steps. First, the generative AI quickly searches the medical database and finds the papers the physician is looking for. Next, the generative AI automatically summarizes the search results and provides them to the physician in an easy-to-understand format. This mechanism greatly simplifies access to and understanding of medical information, and streamlines clinical practice and research activities. First, the generative AI quickly searches the medical database. In this case, the search is performed based on specific keywords or topics that the physician is looking for. For example, when searching for the latest research papers on a specific disease, the generative AI quickly finds relevant papers. Next, the generative AI automatically summarizes the search results. The generative AI analyzes the content of the found papers, extracts important points, and creates a summary. For example, by concisely summarizing the conclusions and main findings of the papers, it enables physicians to grasp the content in a short time. The generated summary is provided to physicians in an easy-to-understand format. For example, it is provided in a visually easy-to-understand format using bullet points and charts. This allows physicians to quickly acquire necessary information and utilize it in clinical practice and research activities. This system enables physicians to rapidly and efficiently obtain and understand the latest medical information. This streamlines clinical practice and research activities, contributing to improved quality of medical care. For example, it allows for the rapid acquisition of information useful for introducing new treatments and improving diagnostic accuracy. Furthermore, the use of generative AI can address the diversity of medical information. Physicians can access and understand a wide range of medical information, not limited to specific fields. This leads to faster and more accurate decision-making in the medical field. Additionally, generative AI makes it easier to understand medical papers. Even papers containing specialized content can be summarized by generative AI, allowing physicians to grasp the content quickly. This reduces the burden on physicians, allowing them to dedicate more time to clinical practice and research activities.In this way, by using generative AI to provide physicians with efficient and rapid search and summarization of medical articles, access to and understanding of medical information is greatly simplified, and clinical practice and research activities become more efficient. This contributes to improving the quality of medical care and reduces the burden on physicians. As a result, medical information search and summarization systems enable physicians to efficiently acquire and understand medical information.

[0029] The medical information retrieval and summarization system according to this embodiment comprises a reception unit, a search unit, a summarization unit, and a provision unit. The reception unit receives input from a physician. Physician input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone or voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera or image recognition technology for receiving image input. For example, the reception unit analyzes the text data entered by the physician and generates an appropriate search query. In the case of voice input, the reception unit uses voice recognition technology to convert the voice data into text data and generates a search query. In the case of image input, the reception unit uses image recognition technology to analyze the image data and generate a search query. The search unit uses generative AI to search the medical database based on the information received by the reception unit. The search unit searches the medical database based on, for example, specific keywords or topics that the physician is looking for. The search unit can use generative AI to quickly find relevant papers. For example, the search unit searches for the latest research papers on a specific disease. The search unit uses generative AI to extract relevant papers from the vast amount of information in the medical database. The summarization unit uses generative AI to summarize the search results obtained by the search unit. For example, the summarization unit analyzes the content of the found papers, extracts key points, and creates a summary. The summarization unit can use generative AI to concisely summarize the conclusions and main findings of the papers. For example, the summarization unit provides the conclusions and main findings of the papers in a visually easy-to-understand format using bullet points and charts. The summarization unit uses generative AI to analyze the content of the papers, extract important information, and create a summary. The delivery unit provides the information summarized by the summarization unit to physicians. For example, the delivery unit provides the summary in formats such as text display, audio output, and graphical display. The delivery unit provides the summary in a visually easy-to-understand format so that physicians can obtain the necessary information in a short time. For example, the delivery unit provides the summary using bullet points and charts. The delivery unit can use generative AI to provide the summary in a format that is easy for physicians to understand.As a result, the medical information retrieval and summarization system according to this embodiment allows physicians to efficiently acquire and understand medical information. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide a summary using an AI model that takes a summary generated by the summarization unit as input and outputs it in a visually easy-to-understand format.

[0030] The reception area receives input from physicians. This input may include, but is not limited to, text input, voice input, and image input. The reception area may, for example, be equipped with a keyboard or touchscreen for text input, allowing physicians to quickly and accurately input necessary information. Furthermore, the reception area may also be equipped with a microphone and speech recognition technology for voice input. Speech recognition technology can convert what the physician says into text in real time, saving time on input. For example, when a physician verbally describes a patient's symptoms or diagnosis, the content is immediately recorded as text data. The reception area may also be equipped with a camera and image recognition technology for image input. Image recognition technology can analyze images taken or scanned documents by physicians and extract necessary information. For example, when a physician takes an X-ray of a patient and inputs the image into the system, image recognition technology identifies abnormalities and generates relevant search queries. This allows the reception area to analyze the text data entered by the physician and generate appropriate search queries. In the case of voice input, the reception desk uses speech recognition technology to convert the voice data into text data and generate a search query. In the case of image input, the reception desk uses image recognition technology to analyze the image data and generate a search query. This allows the reception desk to support various input formats and provide an environment in which doctors can efficiently input information.

[0031] The search unit uses generative AI to search medical databases based on information received by the reception unit. For example, the search unit searches medical databases based on specific keywords or topics that a doctor is looking for. The generative AI utilizes natural language processing technology to understand the doctor's input and generate optimal search queries. For example, if a doctor wants to search for "the latest treatments for diabetes," the generative AI analyzes the doctor's intent and can quickly find relevant papers and research materials. The search unit can quickly find relevant papers using the generative AI. For example, the search unit searches for the latest research papers on a specific disease. The generative AI uses advanced algorithms to extract relevant papers from the vast amount of information in the medical database. This allows the search unit to provide doctors with the information they need quickly and accurately. Furthermore, the search unit updates search results in real time and can respond to the addition of new information. For example, if a new research paper is published, the generative AI can immediately incorporate that information and reflect it in the search results. This allows the search unit to always provide the latest information and assist doctors in selecting the optimal treatment and diagnostic methods.

[0032] The summarization unit uses generative AI to summarize the search results obtained by the search unit. For example, the summarization unit analyzes the content of the found papers, extracts key points, and creates a summary. The generative AI utilizes natural language processing technology to understand the content of the papers and extract important information. For example, the summarization unit can concisely summarize the conclusions and main findings of the papers. The generative AI analyzes the structure of the papers and provides conclusions and main findings in a visually easy-to-understand format using bullet points and charts. This allows the summarization unit to help physicians grasp the necessary information in a short amount of time. Furthermore, the summarization unit uses generative AI to analyze the content of the papers, extract important information, and create a summary. For example, the summarization unit provides conclusions and main findings of the papers in a visually easy-to-understand format using bullet points and charts. The generative AI analyzes the content of the papers, extracts important information, and creates a summary. This allows the summarization unit to help physicians grasp the necessary information in a short amount of time. Furthermore, the summarization unit uses generative AI to analyze the content of the papers, extract important information, and create a summary. For example, the abstract section presents the conclusions and main findings of a paper in a visually easy-to-understand format using bullet points and figures. This allows the abstract section to help physicians grasp the necessary information in a short amount of time.

[0033] The information provider unit provides the information summarized by the summarization unit to the physician. The information provider unit provides the summary in a format such as text display, audio output, or graphical display. The information provider unit provides the summary in a visually easy-to-understand format so that the physician can obtain the necessary information in a short time. For example, the information provider unit provides the summary using bullet points or charts. The information provider unit can use a generating AI to provide the summary in a format that is easy for the physician to understand. As a result, the medical information retrieval and summarization system according to the embodiment allows physicians to efficiently obtain and understand medical information. Some or all of the above-described processing in the information provider unit may be performed using AI, for example, or without AI. For example, the information provider unit can provide the summary using an AI model that takes the summary generated by the summarization unit as input and outputs it in a visually easy-to-understand format. As a result, the information provider unit can provide the summary in a visually easy-to-understand format so that the physician can obtain the necessary information in a short time.

[0034] The search unit can search medical databases based on specific keywords or topics that a physician is looking for. For example, if a physician is looking for the latest research papers on a particular disease, the search unit can quickly find relevant papers. The search unit uses generative AI to extract relevant papers from the vast amount of information in the medical database. For example, the search unit can filter relevant papers based on specific keywords or topics. The search unit can quickly find the information that a physician is looking for using generative AI. This allows physicians to quickly obtain relevant medical information by searching based on specific keywords or topics they are looking for. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input keywords entered by a physician into the generative AI and have the generative AI search for relevant papers.

[0035] The summarization unit can analyze the content of found papers, extract key points, and create summaries. For example, the summarization unit can concisely summarize the conclusions and main findings of a paper. The summarization unit uses generative AI to analyze the content of papers, extract important information, and create summaries. For example, the summarization unit can provide the conclusions and main findings of a paper in a visually easy-to-understand format using bullet points and figures. The summarization unit can use generative AI to analyze the content of papers, extract important information, and create summaries. This allows doctors to grasp the content in a short time by analyzing the content of papers, extracting key points, and creating summaries. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input paper data obtained by the search unit into the generative AI and have the generative AI perform the paper summarization.

[0036] The service provider can provide summaries in a visually easy-to-understand format using bullet points or charts. The service provider can provide summaries in formats such as text display, audio output, or graphical display. The service provider provides summaries in a visually easy-to-understand format so that physicians can quickly obtain the necessary information. For example, the service provider can provide summaries using bullet points or charts. The service provider can use generation AI to provide summaries in a format easily understood by physicians. This allows physicians to quickly obtain the necessary information by providing summaries in a visually easy-to-understand format. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can provide summaries using an AI model that takes summaries generated by the summarization unit as input and outputs them in a visually easy-to-understand format.

[0037] The summary section can concisely summarize the conclusions and main findings of a paper. The summary section provides the conclusions and main findings of a paper in a visually easy-to-understand format, for example, using bullet points or figures and tables. The summary section uses generative AI to analyze the content of the paper, extract important information, and create a summary. For example, the summary section concisely summarizes the conclusions and main findings of a paper. The summary section can use generative AI to analyze the content of a paper, extract important information, and create a summary. This allows physicians to grasp important information quickly by concisely summarizing the conclusions and main findings of a paper. Some or all of the above processing in the summary section may be performed using AI, for example, or without AI. For example, the summary section can input paper data obtained by the search section into the generative AI, and have the generative AI extract the conclusions and main findings of the paper.

[0038] The reception desk can analyze a doctor's past search history and suggest the optimal input method. For example, the reception desk can automatically display keywords and topics that the doctor has frequently searched for in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the doctor has used in the past. The reception desk can also predict and suggest keywords and topics that the doctor will use at specific times of day based on their past search history. In this way, the reception desk can suggest the optimal input method by analyzing the doctor's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the doctor's past search history data into a generating AI and have the generating AI suggest the optimal input method.

[0039] The reception system can automatically complete input suggestions based on the doctor's specialty and interests. For example, the reception system can automatically complete keywords related to the doctor's specialty. The reception system can also present relevant topics as input suggestions based on the doctor's interests. The reception system can also automatically complete relevant keywords based on what the doctor has searched for in the past. This improves input efficiency by automatically completing input suggestions based on the doctor's specialty and interests. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input data on the doctor's specialty and interests into a generating AI and have the generating AI perform automatic completion of input suggestions.

[0040] The reception system can prioritize the input of relevant medical information based on the doctor's geographical location. For example, the reception system can prioritize displaying medical information related to the area the doctor is currently in. The reception system can also suggest medical information related to areas the doctor has visited in the past as input options. The reception system can also automatically complete relevant keywords and topics based on the doctor's geographical location. This allows for the efficient acquisition of region-specific information by prioritizing the input of relevant medical information based on the doctor's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the doctor's geographical location data into a generating AI and have the generating AI prioritize the input of relevant medical information.

[0041] The reception desk can analyze a doctor's social media activity and suggest relevant topics as input candidates. For example, the reception desk can display topics that the doctor frequently mentions on social media as input candidates. The reception desk can also automatically complete keywords of interest based on the doctor's social media activity. The reception desk can also analyze the content of posts from experts that the doctor follows on social media and suggest relevant topics as input candidates. This allows for efficient input of topics of interest by analyzing the doctor's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the doctor's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant topics as input candidates.

[0042] The search unit can optimize its search algorithm by referring to the physician's past search history during a search. For example, the search unit optimizes the search algorithm based on keywords and topics the physician has searched for in the past. The search unit can also prioritize displaying highly relevant articles based on the physician's past search history. The search unit can also analyze the physician's past search patterns and provide optimal search results. This allows the search algorithm to be optimized and highly relevant search results to be provided by referring to the physician's past search history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the physician's past search history data into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0043] The search unit can apply different search algorithms depending on the physician's specialty during a search. For example, the search unit can apply an algorithm that prioritizes keywords related to the physician's specialty. The search unit can also prioritize displaying highly relevant articles based on the physician's specialty. The search unit can also apply different search filters depending on the physician's specialty. This allows for the provision of specialty-specific search results by applying different search algorithms depending on the physician's specialty. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input physician specialty data into a generating AI and have the generating AI execute the application of search algorithms.

[0044] The search unit can prioritize displaying highly relevant papers based on the physician's geographical location information during a search. For example, the search unit can prioritize displaying papers related to the region the physician is currently in. The search unit can also include papers related to regions the physician has visited in the past in its search results. The search unit can also prioritize displaying highly relevant papers based on the physician's geographical location information. This allows for the efficient acquisition of region-specific information by prioritizing the display of highly relevant papers based on the physician's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the physician's geographical location information data into a generating AI and have the generating AI perform the priority display of highly relevant papers.

[0045] The search unit can analyze a physician's social media activity during a search and include relevant papers in the search results. For example, the search unit can include papers related to topics frequently mentioned by the physician on social media. The search unit can also prioritize displaying papers related to keywords of interest based on the physician's social media activity. The search unit can also analyze posts from experts that the physician follows on social media and include relevant papers in the search results. This allows for the efficient retrieval of papers related to topics of interest by analyzing the physician's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can input the physician's social media activity data into a generating AI and have the generating AI perform the inclusion of relevant papers in the search results.

[0046] The abstracting unit can adjust the level of detail in the summary based on the importance of the paper during summary generation. For example, the abstracting unit provides a detailed summary for important papers. For general papers, the abstracting unit can also provide a concise summary. The abstracting unit can also adjust the level of detail in the summary based on the interests of the physician. This allows for a detailed understanding of important information by adjusting the level of detail in the summary based on the importance of the paper. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0047] The abstracting unit can apply different summarization algorithms depending on the category of the paper when generating the abstract. For example, in the case of a paper on clinical research, the abstracting unit can provide an abstract that emphasizes the clinical points. In the case of a paper on basic research, the abstracting unit can also provide an abstract that explains the background and methods of the research in detail. In the case of a paper on medical technology, the abstracting unit can also provide an abstract that includes technical details. This allows for the provision of a more appropriate abstract by applying the most suitable summarization algorithm according to the category of the paper. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or without AI. For example, the abstracting unit can input paper category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0048] The abstracting unit can determine the priority of summaries based on the publication date of the papers when generating summaries. For example, the abstracting unit may prioritize summarizing the most recent papers. It can also prioritize summarizing important papers from the past. The abstracting unit can also adjust the priority of summaries based on the interests of physicians. This allows for priority access to the latest information by prioritizing summaries based on the publication date of the papers. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper publication date data into a generating AI and have the generating AI perform the determination of the summary priority.

[0049] The abstracting unit can adjust the order of summaries based on the relevance of the papers during the summaries generation process. For example, the abstracting unit can prioritize summarizing papers related to a specific topic that a physician is looking for. It can also prioritize summarizing papers related to the physician's specialty. The abstracting unit can also adjust the order of summaries based on the physician's interests. This allows for prioritizing the identification of highly relevant information by adjusting the order of summaries based on the relevance of the papers. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper relevance data into a generating AI and have the generating AI perform the adjustment of the summaries' order.

[0050] The service provider can select the optimal display method by referring to the physician's past usage history at the time of provision. For example, the service provider can provide the optimal display method based on the display methods the physician has used in the past. The service provider can also select a display method with high visibility from the physician's past usage history. The service provider can also analyze the physician's past usage patterns and propose the optimal display method. In this way, the optimal display method can be provided by referring to the physician's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the physician's past usage history data into a generating AI and have the generating AI select the optimal display method.

[0051] The service provider can apply different display formats depending on the physician's specialty at the time of delivery. For example, the service provider can provide a display format that highlights information related to the physician's specialty. The service provider can also prioritize the display of highly relevant information based on the physician's specialty. The service provider can also apply different display filters depending on the physician's specialty. This allows for the provision of specialty-specific information by applying different display formats depending on the physician's specialty. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input physician specialty data into a generating AI and have the generating AI perform the application of display formats.

[0052] The information provider can prioritize displaying highly relevant information based on the physician's geographical location information at the time of provision. For example, the information provider can prioritize displaying information related to the area the physician is currently in. The information provider can also provide information related to areas the physician has visited in the past. The information provider can also prioritize displaying highly relevant information based on the physician's geographical location information. This allows for the efficient acquisition of region-specific information by prioritizing the display of highly relevant information based on the physician's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the physician's geographical location information data into a generating AI and have the generating AI perform the priority display of highly relevant information.

[0053] The information provider can analyze a physician's social media activity and provide relevant information at the time of delivery. For example, the provider can provide information related to topics that physicians frequently mention on social media. The provider can also prioritize displaying information related to keywords of interest from the physician's social media activity. The provider can also analyze the content of posts from experts that physicians follow on social media and provide relevant information. This allows for the efficient acquisition of information related to topics of interest by analyzing the physician's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the physician's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The search unit can analyze a physician's past search history and provide optimal search results. For example, it can prioritize displaying relevant papers based on keywords and topics that the physician has frequently searched in the past. It can also analyze a physician's past search patterns and provide optimal search results. This allows the search algorithm to be optimized by referring to the physician's past search history, providing highly relevant search results. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can input the physician's past search history data into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0056] The reception desk can automatically complete input suggestions based on the doctor's specialty and interests. For example, it can automatically complete keywords related to the doctor's specialty. It can also suggest related topics as input suggestions based on the doctor's interests. It can also automatically complete related keywords based on what the doctor has searched for in the past. This improves input efficiency by automatically completing input suggestions based on the doctor's specialty and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the doctor's specialty and interests into a generating AI and have the generating AI perform automatic completion of input suggestions.

[0057] The search unit can apply different search algorithms depending on the physician's specialty. For example, it can apply an algorithm that prioritizes searching for keywords related to the physician's specialty. It can also prioritize displaying highly relevant papers based on the physician's specialty. Different search filters can also be applied depending on the physician's specialty. This allows for the provision of specialty-specific search results by applying different search algorithms depending on the physician's specialty. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input physician specialty data into a generating AI and have the generating AI execute the application of search algorithms.

[0058] The abstracting unit can apply different summarization algorithms depending on the category of the paper when generating the abstract. For example, for papers on clinical research, it can provide an abstract that emphasizes the clinical points. For papers on basic research, it can also provide an abstract that explains the research background and methods in detail. For papers on medical technology, it can also provide an abstract that includes technical details. This allows for the provision of more appropriate summaries by applying the most suitable summarization algorithm according to the category of the paper. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0059] The service provider can select the optimal display method by referring to the physician's past usage history at the time of service provision. For example, it can provide the optimal display method based on the display methods the physician has used in the past. It can also select a display method with high visibility from the physician's past usage history. It can also analyze the physician's past usage patterns and propose the optimal display method. In this way, the optimal display method can be provided by referring to the physician's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the physician's past usage history data into a generating AI and have the generating AI select the optimal display method.

[0060] The search unit can prioritize displaying highly relevant papers based on the physician's geographical location. For example, it can prioritize displaying papers related to the region the physician is currently in. It can also include papers related to regions the physician has visited in the past in the search results. It can also prioritize displaying highly relevant papers based on the physician's geographical location. This allows for the efficient acquisition of region-specific information by prioritizing the display of highly relevant papers based on the physician's geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the physician's geographical location data into a generating AI and have the generating AI perform the priority display of highly relevant papers.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives input from the doctor. This input includes text input, voice input, and image input. The reception desk is equipped with a keyboard or touchscreen for text input, a microphone and voice recognition technology for voice input, and a camera and image recognition technology for image input. For example, the reception desk analyzes the text data entered by the doctor and generates an appropriate search query. In the case of voice input, voice recognition technology is used to convert the voice data into text data and generate a search query. In the case of image input, image recognition technology is used to analyze the image data and generate a search query. Step 2: The search unit searches the medical database based on the information received by the reception unit. The search unit uses generative AI to search the medical database based on specific keywords and topics that the doctor is looking for, and quickly finds relevant papers. For example, it searches for the latest research papers on a specific disease. The search unit uses generative AI to extract relevant papers from the vast amount of information in the medical database. Step 3: The summary section summarizes the search results obtained by the search section. The summary section uses generative AI to analyze the content of the found papers, extract key points, and create a summary. For example, it concisely summarizes the conclusions and main findings of the papers. The summary section provides the conclusions and main findings of the papers in a visually easy-to-understand format using bullet points and figures / tables. Step 4: The delivery unit provides the information summarized by the summarization unit to the physician. The delivery unit provides the summary in various formats, such as text display, audio output, and graphical display. For example, the summary can be provided in a visually easy-to-understand format using bullet points or charts. The delivery unit can use generating AI to provide the summary in a format that is easy for physicians to understand.

[0063] (Example of form 2) The medical information retrieval and summarization system according to an embodiment of the present invention is a system that uses generative AI to provide physicians with efficient and rapid search and summarization of medical papers. This system accepts input from physicians, searches a medical database, and provides a summary of the search results, enabling physicians to efficiently acquire medical information. This system consists of the following steps. First, the generative AI quickly searches the medical database and finds the papers the physician is looking for. Next, the generative AI automatically summarizes the search results and provides them to the physician in an easy-to-understand format. This mechanism greatly simplifies access to and understanding of medical information, and streamlines clinical practice and research activities. First, the generative AI quickly searches the medical database. In this case, the search is performed based on specific keywords or topics that the physician is looking for. For example, when searching for the latest research papers on a specific disease, the generative AI quickly finds relevant papers. Next, the generative AI automatically summarizes the search results. The generative AI analyzes the content of the found papers, extracts important points, and creates a summary. For example, by concisely summarizing the conclusions and main findings of the papers, it enables physicians to grasp the content in a short time. The generated summary is provided to physicians in an easy-to-understand format. For example, it is provided in a visually easy-to-understand format using bullet points and charts. This allows physicians to quickly acquire necessary information and utilize it in clinical practice and research activities. This system enables physicians to rapidly and efficiently obtain and understand the latest medical information. This streamlines clinical practice and research activities, contributing to improved quality of medical care. For example, it allows for the rapid acquisition of information useful for introducing new treatments and improving diagnostic accuracy. Furthermore, the use of generative AI can address the diversity of medical information. Physicians can access and understand a wide range of medical information, not limited to specific fields. This leads to faster and more accurate decision-making in the medical field. Additionally, generative AI makes it easier to understand medical papers. Even papers containing specialized content can be summarized by generative AI, allowing physicians to grasp the content quickly. This reduces the burden on physicians, allowing them to dedicate more time to clinical practice and research activities.In this way, by using generative AI to provide physicians with efficient and rapid search and summarization of medical articles, access to and understanding of medical information is greatly simplified, and clinical practice and research activities become more efficient. This contributes to improving the quality of medical care and reduces the burden on physicians. As a result, medical information search and summarization systems enable physicians to efficiently acquire and understand medical information.

[0064] The medical information retrieval and summarization system according to this embodiment comprises a reception unit, a search unit, a summarization unit, and a provision unit. The reception unit receives input from a physician. Physician input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone or voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera or image recognition technology for receiving image input. For example, the reception unit analyzes the text data entered by the physician and generates an appropriate search query. In the case of voice input, the reception unit uses voice recognition technology to convert the voice data into text data and generates a search query. In the case of image input, the reception unit uses image recognition technology to analyze the image data and generate a search query. The search unit uses generative AI to search the medical database based on the information received by the reception unit. The search unit searches the medical database based on, for example, specific keywords or topics that the physician is looking for. The search unit can use generative AI to quickly find relevant papers. For example, the search unit searches for the latest research papers on a specific disease. The search unit uses generative AI to extract relevant papers from the vast amount of information in the medical database. The summarization unit uses generative AI to summarize the search results obtained by the search unit. For example, the summarization unit analyzes the content of the found papers, extracts key points, and creates a summary. The summarization unit can use generative AI to concisely summarize the conclusions and main findings of the papers. For example, the summarization unit provides the conclusions and main findings of the papers in a visually easy-to-understand format using bullet points and charts. The summarization unit uses generative AI to analyze the content of the papers, extract important information, and create a summary. The delivery unit provides the information summarized by the summarization unit to physicians. For example, the delivery unit provides the summary in formats such as text display, audio output, and graphical display. The delivery unit provides the summary in a visually easy-to-understand format so that physicians can obtain the necessary information in a short time. For example, the delivery unit provides the summary using bullet points and charts. The delivery unit can use generative AI to provide the summary in a format that is easy for physicians to understand.As a result, the medical information retrieval and summarization system according to this embodiment allows physicians to efficiently acquire and understand medical information. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide a summary using an AI model that takes a summary generated by the summarization unit as input and outputs it in a visually easy-to-understand format.

[0065] The reception area receives input from physicians. This input may include, but is not limited to, text input, voice input, and image input. The reception area may, for example, be equipped with a keyboard or touchscreen for text input, allowing physicians to quickly and accurately input necessary information. Furthermore, the reception area may also be equipped with a microphone and speech recognition technology for voice input. Speech recognition technology can convert what the physician says into text in real time, saving time on input. For example, when a physician verbally describes a patient's symptoms or diagnosis, the content is immediately recorded as text data. The reception area may also be equipped with a camera and image recognition technology for image input. Image recognition technology can analyze images taken or scanned documents by physicians and extract necessary information. For example, when a physician takes an X-ray of a patient and inputs the image into the system, image recognition technology identifies abnormalities and generates relevant search queries. This allows the reception area to analyze the text data entered by the physician and generate appropriate search queries. In the case of voice input, the reception desk uses speech recognition technology to convert the voice data into text data and generate a search query. In the case of image input, the reception desk uses image recognition technology to analyze the image data and generate a search query. This allows the reception desk to support various input formats and provide an environment in which doctors can efficiently input information.

[0066] The search unit uses generative AI to search medical databases based on information received by the reception unit. For example, the search unit searches medical databases based on specific keywords or topics that a doctor is looking for. The generative AI utilizes natural language processing technology to understand the doctor's input and generate optimal search queries. For example, if a doctor wants to search for "the latest treatments for diabetes," the generative AI analyzes the doctor's intent and can quickly find relevant papers and research materials. The search unit can quickly find relevant papers using the generative AI. For example, the search unit searches for the latest research papers on a specific disease. The generative AI uses advanced algorithms to extract relevant papers from the vast amount of information in the medical database. This allows the search unit to provide doctors with the information they need quickly and accurately. Furthermore, the search unit updates search results in real time and can respond to the addition of new information. For example, if a new research paper is published, the generative AI can immediately incorporate that information and reflect it in the search results. This allows the search unit to always provide the latest information and assist doctors in selecting the optimal treatment and diagnostic methods.

[0067] The summarization unit uses generative AI to summarize the search results obtained by the search unit. For example, the summarization unit analyzes the content of the found papers, extracts key points, and creates a summary. The generative AI utilizes natural language processing technology to understand the content of the papers and extract important information. For example, the summarization unit can concisely summarize the conclusions and main findings of the papers. The generative AI analyzes the structure of the papers and provides conclusions and main findings in a visually easy-to-understand format using bullet points and charts. This allows the summarization unit to help physicians grasp the necessary information in a short amount of time. Furthermore, the summarization unit uses generative AI to analyze the content of the papers, extract important information, and create a summary. For example, the summarization unit provides conclusions and main findings of the papers in a visually easy-to-understand format using bullet points and charts. The generative AI analyzes the content of the papers, extracts important information, and creates a summary. This allows the summarization unit to help physicians grasp the necessary information in a short amount of time. Furthermore, the summarization unit uses generative AI to analyze the content of the papers, extract important information, and create a summary. For example, the abstract section presents the conclusions and main findings of a paper in a visually easy-to-understand format using bullet points and figures. This allows the abstract section to help physicians grasp the necessary information in a short amount of time.

[0068] The information provider unit provides the information summarized by the summarization unit to the physician. The information provider unit provides the summary in a format such as text display, audio output, or graphical display. The information provider unit provides the summary in a visually easy-to-understand format so that the physician can obtain the necessary information in a short time. For example, the information provider unit provides the summary using bullet points or charts. The information provider unit can use a generating AI to provide the summary in a format that is easy for the physician to understand. As a result, the medical information retrieval and summarization system according to the embodiment allows physicians to efficiently obtain and understand medical information. Some or all of the above-described processing in the information provider unit may be performed using AI, for example, or without AI. For example, the information provider unit can provide the summary using an AI model that takes the summary generated by the summarization unit as input and outputs it in a visually easy-to-understand format. As a result, the information provider unit can provide the summary in a visually easy-to-understand format so that the physician can obtain the necessary information in a short time.

[0069] The search unit can search medical databases based on specific keywords or topics that a physician is looking for. For example, if a physician is looking for the latest research papers on a particular disease, the search unit can quickly find relevant papers. The search unit uses generative AI to extract relevant papers from the vast amount of information in the medical database. For example, the search unit can filter relevant papers based on specific keywords or topics. The search unit can quickly find the information that a physician is looking for using generative AI. This allows physicians to quickly obtain relevant medical information by searching based on specific keywords or topics they are looking for. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input keywords entered by a physician into the generative AI and have the generative AI search for relevant papers.

[0070] The summarization unit can analyze the content of found papers, extract key points, and create summaries. For example, the summarization unit can concisely summarize the conclusions and main findings of a paper. The summarization unit uses generative AI to analyze the content of papers, extract important information, and create summaries. For example, the summarization unit can provide the conclusions and main findings of a paper in a visually easy-to-understand format using bullet points and figures. The summarization unit can use generative AI to analyze the content of papers, extract important information, and create summaries. This allows doctors to grasp the content in a short time by analyzing the content of papers, extracting key points, and creating summaries. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input paper data obtained by the search unit into the generative AI and have the generative AI perform the paper summarization.

[0071] The service provider can provide summaries in a visually easy-to-understand format using bullet points or charts. The service provider can provide summaries in formats such as text display, audio output, or graphical display. The service provider provides summaries in a visually easy-to-understand format so that physicians can quickly obtain the necessary information. For example, the service provider can provide summaries using bullet points or charts. The service provider can use generation AI to provide summaries in a format easily understood by physicians. This allows physicians to quickly obtain the necessary information by providing summaries in a visually easy-to-understand format. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can provide summaries using an AI model that takes summaries generated by the summarization unit as input and outputs them in a visually easy-to-understand format.

[0072] The summary section can concisely summarize the conclusions and main findings of a paper. The summary section provides the conclusions and main findings of a paper in a visually easy-to-understand format, for example, using bullet points or figures and tables. The summary section uses generative AI to analyze the content of the paper, extract important information, and create a summary. For example, the summary section concisely summarizes the conclusions and main findings of a paper. The summary section can use generative AI to analyze the content of a paper, extract important information, and create a summary. This allows physicians to grasp important information quickly by concisely summarizing the conclusions and main findings of a paper. Some or all of the above processing in the summary section may be performed using AI, for example, or without AI. For example, the summary section can input paper data obtained by the search section into the generative AI, and have the generative AI extract the conclusions and main findings of the paper.

[0073] The reception desk can estimate the doctor's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the doctor is stressed, the reception desk can provide a simple interface and minimize the input steps. If the doctor is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the doctor is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This allows doctors to input comfortably by adjusting the display of the input interface according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the doctor's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can analyze a doctor's past search history and suggest the optimal input method. For example, the reception desk can automatically display keywords and topics that the doctor has frequently searched for in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the doctor has used in the past. The reception desk can also predict and suggest keywords and topics that the doctor will use at specific times of day based on their past search history. In this way, the reception desk can suggest the optimal input method by analyzing the doctor's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the doctor's past search history data into a generating AI and have the generating AI suggest the optimal input method.

[0075] The reception system can automatically complete input suggestions based on the doctor's specialty and interests. For example, the reception system can automatically complete keywords related to the doctor's specialty. The reception system can also present relevant topics as input suggestions based on the doctor's interests. The reception system can also automatically complete relevant keywords based on what the doctor has searched for in the past. This improves input efficiency by automatically completing input suggestions based on the doctor's specialty and interests. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input data on the doctor's specialty and interests into a generating AI and have the generating AI perform automatic completion of input suggestions.

[0076] The reception desk can estimate the doctor's emotions and prioritize inputs based on the estimated emotions. For example, if the doctor is stressed, the reception desk will prioritize displaying important input items. If the doctor is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the doctor is in a hurry, the reception desk can also prioritize inputting the most important information. This allows for the priority of inputting important information by prioritizing inputs according to the doctor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the doctor's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception system can prioritize the input of relevant medical information based on the doctor's geographical location. For example, the reception system can prioritize displaying medical information related to the area the doctor is currently in. The reception system can also suggest medical information related to areas the doctor has visited in the past as input options. The reception system can also automatically complete relevant keywords and topics based on the doctor's geographical location. This allows for the efficient acquisition of region-specific information by prioritizing the input of relevant medical information based on the doctor's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the doctor's geographical location data into a generating AI and have the generating AI prioritize the input of relevant medical information.

[0078] The reception desk can analyze a doctor's social media activity and suggest relevant topics as input candidates. For example, the reception desk can display topics that the doctor frequently mentions on social media as input candidates. The reception desk can also automatically complete keywords of interest based on the doctor's social media activity. The reception desk can also analyze the content of posts from experts that the doctor follows on social media and suggest relevant topics as input candidates. This allows for efficient input of topics of interest by analyzing the doctor's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the doctor's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant topics as input candidates.

[0079] The search unit can estimate the doctor's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the doctor is stressed, the search unit can display simple and highly visible search results. If the doctor is relaxed, the search unit can also display search results containing detailed information. If the doctor is in a hurry, the search unit can also display concise search results. This allows doctors to comfortably review search results by adjusting how they are displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input the doctor's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The search unit can optimize its search algorithm by referring to the physician's past search history during a search. For example, the search unit optimizes the search algorithm based on keywords and topics the physician has searched for in the past. The search unit can also prioritize displaying highly relevant articles based on the physician's past search history. The search unit can also analyze the physician's past search patterns and provide optimal search results. This allows the search algorithm to be optimized and highly relevant search results to be provided by referring to the physician's past search history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the physician's past search history data into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0081] The search unit can apply different search algorithms depending on the physician's specialty during a search. For example, the search unit can apply an algorithm that prioritizes keywords related to the physician's specialty. The search unit can also prioritize displaying highly relevant articles based on the physician's specialty. The search unit can also apply different search filters depending on the physician's specialty. This allows for the provision of specialty-specific search results by applying different search algorithms depending on the physician's specialty. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input physician specialty data into a generating AI and have the generating AI execute the application of search algorithms.

[0082] The search unit can estimate a doctor's emotions and prioritize search results based on the estimated emotions. For example, if a doctor is stressed, the search unit will prioritize displaying important search results. If a doctor is relaxed, the search unit may also display search results containing detailed information. If a doctor is in a hurry, the search unit may also prioritize displaying the most relevant search results. This allows for the prioritization of important information by determining the priority of search results according to the doctor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not using AI. For example, the search unit can input a doctor's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The search unit can prioritize displaying highly relevant papers based on the physician's geographical location information during a search. For example, the search unit can prioritize displaying papers related to the region the physician is currently in. The search unit can also include papers related to regions the physician has visited in the past in its search results. The search unit can also prioritize displaying highly relevant papers based on the physician's geographical location information. This allows for the efficient acquisition of region-specific information by prioritizing the display of highly relevant papers based on the physician's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the physician's geographical location information data into a generating AI and have the generating AI perform the priority display of highly relevant papers.

[0084] The search unit can analyze a physician's social media activity during a search and include relevant papers in the search results. For example, the search unit can include papers related to topics frequently mentioned by the physician on social media. The search unit can also prioritize displaying papers related to keywords of interest based on the physician's social media activity. The search unit can also analyze posts from experts that the physician follows on social media and include relevant papers in the search results. This allows for the efficient retrieval of papers related to topics of interest by analyzing the physician's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can input the physician's social media activity data into a generating AI and have the generating AI perform the inclusion of relevant papers in the search results.

[0085] The summarization unit can estimate the physician's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the physician is stressed, the summarization unit provides a simple and easy-to-read summary. If the physician is relaxed, the summarization unit can also provide a summary with more detailed information. If the physician is in a hurry, the summarization unit can provide a concise summary. This allows the physician to comfortably review the summary by adjusting its presentation according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input the physician's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The abstracting unit can adjust the level of detail in the summary based on the importance of the paper during summary generation. For example, the abstracting unit provides a detailed summary for important papers. For general papers, the abstracting unit can also provide a concise summary. The abstracting unit can also adjust the level of detail in the summary based on the interests of the physician. This allows for a detailed understanding of important information by adjusting the level of detail in the summary based on the importance of the paper. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0087] The abstracting unit can apply different summarization algorithms depending on the category of the paper when generating the abstract. For example, in the case of a paper on clinical research, the abstracting unit can provide an abstract that emphasizes the clinical points. In the case of a paper on basic research, the abstracting unit can also provide an abstract that explains the background and methods of the research in detail. In the case of a paper on medical technology, the abstracting unit can also provide an abstract that includes technical details. This allows for the provision of a more appropriate abstract by applying the most suitable summarization algorithm according to the category of the paper. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or without AI. For example, the abstracting unit can input paper category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0088] The summarization unit can estimate the physician's emotions and adjust the length of the summary based on the estimated emotions. For example, if the physician is stressed, the summarization unit will provide a short, concise summary. If the physician is relaxed, the summarization unit may also provide a longer summary with more detailed explanations. If the physician is in a hurry, the summarization unit may also provide a brief, concise summary. This allows the physician to comfortably review the summary by adjusting its length according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input the physician's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The abstracting unit can determine the priority of summaries based on the publication date of the papers when generating summaries. For example, the abstracting unit may prioritize summarizing the most recent papers. It can also prioritize summarizing important papers from the past. The abstracting unit can also adjust the priority of summaries based on the interests of physicians. This allows for priority access to the latest information by prioritizing summaries based on the publication date of the papers. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper publication date data into a generating AI and have the generating AI perform the determination of the summary priority.

[0090] The abstracting unit can adjust the order of summaries based on the relevance of the papers during the summaries generation process. For example, the abstracting unit can prioritize summarizing papers related to a specific topic that a physician is looking for. It can also prioritize summarizing papers related to the physician's specialty. The abstracting unit can also adjust the order of summaries based on the physician's interests. This allows for prioritizing the identification of highly relevant information by adjusting the order of summaries based on the relevance of the papers. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper relevance data into a generating AI and have the generating AI perform the adjustment of the summaries' order.

[0091] The information provider can estimate the physician's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the physician is stressed, the provider can provide a simple and highly visible display method. If the physician is relaxed, the provider can also provide a display method that includes detailed information. If the physician is in a hurry, the provider can also provide a display method that gets straight to the point. This allows the physician to comfortably review the information by adjusting the way the information is displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input the physician's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can select the optimal display method by referring to the physician's past usage history at the time of provision. For example, the service provider can provide the optimal display method based on the display methods the physician has used in the past. The service provider can also select a display method with high visibility from the physician's past usage history. The service provider can also analyze the physician's past usage patterns and propose the optimal display method. In this way, the optimal display method can be provided by referring to the physician's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the physician's past usage history data into a generating AI and have the generating AI select the optimal display method.

[0093] The service provider can apply different display formats depending on the physician's specialty at the time of delivery. For example, the service provider can provide a display format that highlights information related to the physician's specialty. The service provider can also prioritize the display of highly relevant information based on the physician's specialty. The service provider can also apply different display filters depending on the physician's specialty. This allows for the provision of specialty-specific information by applying different display formats depending on the physician's specialty. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input physician specialty data into a generating AI and have the generating AI perform the application of display formats.

[0094] The information provider can estimate the physician's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the physician is stressed, the provider will prioritize displaying important information. If the physician is relaxed, the provider can also provide a display method that includes detailed information. If the physician is in a hurry, the provider can also prioritize displaying the most relevant information. This ensures that important information is prioritized by prioritizing information according to the physician's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input the physician's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The information provider can prioritize displaying highly relevant information based on the physician's geographical location information at the time of provision. For example, the information provider can prioritize displaying information related to the area the physician is currently in. The information provider can also provide information related to areas the physician has visited in the past. The information provider can also prioritize displaying highly relevant information based on the physician's geographical location information. This allows for the efficient acquisition of region-specific information by prioritizing the display of highly relevant information based on the physician's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the physician's geographical location information data into a generating AI and have the generating AI perform the priority display of highly relevant information.

[0096] The information provider can analyze a physician's social media activity and provide relevant information at the time of delivery. For example, the provider can provide information related to topics that physicians frequently mention on social media. The provider can also prioritize displaying information related to keywords of interest from the physician's social media activity. The provider can also analyze the content of posts from experts that physicians follow on social media and provide relevant information. This allows for the efficient acquisition of information related to topics of interest by analyzing the physician's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the physician's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the doctor's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the doctor is stressed, a simple interface can be provided, minimizing the input steps. If the doctor is relaxed, detailed input options can be provided, and customizable input methods can be suggested. If the doctor is in a hurry, voice input can be prioritized to allow for quick information entry. This allows doctors to input comfortably by adjusting the display of the input interface according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the doctor's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The search unit can analyze a physician's past search history and provide optimal search results. For example, it can prioritize displaying relevant papers based on keywords and topics that the physician has frequently searched in the past. It can also analyze a physician's past search patterns and provide optimal search results. This allows the search algorithm to be optimized by referring to the physician's past search history, providing highly relevant search results. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can input the physician's past search history data into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0100] The summarization unit can estimate the physician's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the physician is stressed, it can provide a simple and easy-to-read summary. If the physician is relaxed, it can provide a summary with more detailed information. If the physician is in a hurry, it can provide a summary that gets straight to the point. This allows the physician to comfortably review the summary by adjusting its presentation according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input the physician's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0101] The information provider can estimate the physician's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the physician is stressed, a simple and highly visible display method can be provided. If the physician is relaxed, a display method including detailed information can be provided. If the physician is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the way information is displayed according to the physician's emotions, the physician can comfortably review the information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the physician's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The reception desk can automatically complete input suggestions based on the doctor's specialty and interests. For example, it can automatically complete keywords related to the doctor's specialty. It can also suggest related topics as input suggestions based on the doctor's interests. It can also automatically complete related keywords based on what the doctor has searched for in the past. This improves input efficiency by automatically completing input suggestions based on the doctor's specialty and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the doctor's specialty and interests into a generating AI and have the generating AI perform automatic completion of input suggestions.

[0103] The search unit can apply different search algorithms depending on the physician's specialty. For example, it can apply an algorithm that prioritizes searching for keywords related to the physician's specialty. It can also prioritize displaying highly relevant papers based on the physician's specialty. Different search filters can also be applied depending on the physician's specialty. This allows for the provision of specialty-specific search results by applying different search algorithms depending on the physician's specialty. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input physician specialty data into a generating AI and have the generating AI execute the application of search algorithms.

[0104] The abstracting unit can apply different summarization algorithms depending on the category of the paper when generating the abstract. For example, for papers on clinical research, it can provide an abstract that emphasizes the clinical points. For papers on basic research, it can also provide an abstract that explains the research background and methods in detail. For papers on medical technology, it can also provide an abstract that includes technical details. This allows for the provision of more appropriate summaries by applying the most suitable summarization algorithm according to the category of the paper. Some or all of the above processing in the abstracting unit may be performed using AI, for example, or not using AI. For example, the abstracting unit can input paper category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0105] The service provider can select the optimal display method by referring to the physician's past usage history at the time of service provision. For example, it can provide the optimal display method based on the display methods the physician has used in the past. It can also select a display method with high visibility from the physician's past usage history. It can also analyze the physician's past usage patterns and propose the optimal display method. In this way, the optimal display method can be provided by referring to the physician's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the physician's past usage history data into a generating AI and have the generating AI select the optimal display method.

[0106] The information provider can estimate the doctor's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the doctor is stressed, important information may be displayed preferentially. If the doctor is relaxed, a display method including detailed information may be provided. If the doctor is in a hurry, the most relevant information may be displayed preferentially. This allows important information to be displayed preferentially by prioritizing information according to the doctor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input the doctor's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The search unit can prioritize displaying highly relevant papers based on the physician's geographical location. For example, it can prioritize displaying papers related to the region the physician is currently in. It can also include papers related to regions the physician has visited in the past in the search results. It can also prioritize displaying highly relevant papers based on the physician's geographical location. This allows for the efficient acquisition of region-specific information by prioritizing the display of highly relevant papers based on the physician's geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the physician's geographical location data into a generating AI and have the generating AI perform the priority display of highly relevant papers.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives input from the doctor. This input includes text input, voice input, and image input. The reception desk is equipped with a keyboard or touchscreen for text input, a microphone and voice recognition technology for voice input, and a camera and image recognition technology for image input. For example, the reception desk analyzes the text data entered by the doctor and generates an appropriate search query. In the case of voice input, voice recognition technology is used to convert the voice data into text data and generate a search query. In the case of image input, image recognition technology is used to analyze the image data and generate a search query. Step 2: The search unit searches the medical database based on the information received by the reception unit. The search unit uses generative AI to search the medical database based on specific keywords and topics that the doctor is looking for, and quickly finds relevant papers. For example, it searches for the latest research papers on a specific disease. The search unit uses generative AI to extract relevant papers from the vast amount of information in the medical database. Step 3: The summary section summarizes the search results obtained by the search section. The summary section uses generative AI to analyze the content of the found papers, extract key points, and create a summary. For example, it concisely summarizes the conclusions and main findings of the papers. The summary section provides the conclusions and main findings of the papers in a visually easy-to-understand format using bullet points and figures / tables. Step 4: The delivery unit provides the information summarized by the summarization unit to the physician. The delivery unit provides the summary in various formats, such as text display, audio output, and graphical display. For example, the summary can be provided in a visually easy-to-understand format using bullet points or charts. The delivery unit can use generating AI to provide the summary in a format that is easy for physicians to understand.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, search unit, summarization unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from a physician. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches a medical database using a generation AI. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the search results using a generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the summarized information to the physician. The reception unit can estimate the physician's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, search unit, summarization unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from a physician. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches a medical database using a generation AI. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the search results using a generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the summarized information to the physician. The reception unit can estimate the physician's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, search unit, summarization unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from a physician. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches a medical database using a generation AI. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the search results using a generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the summarized information to the physician. The reception unit can estimate the physician's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0162] Each of the multiple elements described above, including the reception unit, search unit, summarization unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from a physician. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches a medical database using a generation AI. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the search results using a generation AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the summarized information to the physician. The reception unit can estimate the physician's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The reception area where doctors' information is received, A search unit that searches a medical database based on the information received by the reception unit, A summarization unit that summarizes the search results obtained by the search unit, The system includes a providing unit that provides the information summarized by the summarizing unit to a physician. A system characterized by the following features. (Note 2) The aforementioned search unit, Search medical databases based on specific keywords or topics that doctors are looking for. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary section above is, Analyze the content of the found papers, extract the key points, and create a summary. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The summary is provided in a visually easy-to-understand format using bullet points and charts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The summary section above is, Summarize the conclusions and main findings of the paper concisely. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the doctor's emotions and adjusts the display of the input interface based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the doctor's past search history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system automatically completes input suggestions based on the doctor's specialty and interests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the doctor's emotions and determines the priority of inputs based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is This system allows for the priority input of relevant medical information based on the doctor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze doctors' social media activity and suggest relevant topics as input options. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, We estimate the doctor's emotions and adjust how search results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, During a search, the search algorithm is optimized by referencing the doctor's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, different search algorithms are applied depending on the doctor's specialty. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, The system estimates the doctor's emotions and prioritizes search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the system prioritizes displaying highly relevant articles based on the doctor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, we analyze doctors' social media activity and include relevant papers in the search results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, The system estimates the doctor's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, When generating the summary, adjust the level of detail in the summary based on the importance of the paper. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the paper. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary section above is, Estimate the doctor's emotions and adjust the length of the summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary section above is, When generating summaries, the priority of summaries is determined based on the publication date of the paper. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the papers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the doctor's emotions and adjusts how the information provided is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the optimal display method is selected by referring to the doctor's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, different display formats will be applied depending on the physician's specialty. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the doctor's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the system prioritizes displaying highly relevant information based on the physician's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the doctor's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where doctors' information is received, A search unit that searches a medical database based on the information received by the reception unit, A summarization unit that summarizes the search results obtained by the search unit, The system includes a providing unit that provides the information summarized by the summarizing unit to a physician. A system characterized by the following features.

2. The aforementioned search unit, Search medical databases based on specific keywords or topics that doctors are looking for. The system according to feature 1.

3. The summary section above is, Analyze the content of the found papers, extract the key points, and create a summary. The system according to feature 1.

4. The aforementioned supply unit is, The summary is provided in a visually easy-to-understand format using bullet points and charts. The system according to feature 1.

5. The summary section above is, Summarize the conclusions and main findings of the paper concisely. The system according to feature 1.

6. The aforementioned reception unit is It estimates the doctor's emotions and adjusts the display of the input interface based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the doctor's past search history and suggest the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is The system automatically completes input suggestions based on the doctor's specialty and interests. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the doctor's emotions and determines the priority of inputs based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is This system allows for the priority input of relevant medical information based on the doctor's geographical location. The system according to feature 1.

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